Hyper-MML integrates EEG, audio, and video using an Adaptive Brain Encoder with Mutual-cross Attention (ABEMA) and Adaptive Hypergraph Fusion Module (AHFM) to outperform prior methods on EAV and AFFEC datasets for conversational emotion recognition.
Neural-mcrl: Neural multimodal contrastive representation learning for eeg-based visual decoding
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MB2L achieves 80.5% top-1 and 97.6% top-5 accuracy on zero-shot EEG-to-image retrieval by using biomimetic modules and bidirectional contrastive learning to align neural and visual features.
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Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation
Hyper-MML integrates EEG, audio, and video using an Adaptive Brain Encoder with Mutual-cross Attention (ABEMA) and Adaptive Hypergraph Fusion Module (AHFM) to outperform prior methods on EAV and AFFEC datasets for conversational emotion recognition.
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Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding
MB2L achieves 80.5% top-1 and 97.6% top-5 accuracy on zero-shot EEG-to-image retrieval by using biomimetic modules and bidirectional contrastive learning to align neural and visual features.